POS1328 DIFFERENTIATING “SCLERODERMA” WITH “NON-SCLERODERMA” PATTERNS IN NAILFOLD CAPILLARY MICROSCOPY USING A DEEP LEARNING MODEL
Authors: V. Korendovych, P. Korsten
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Nailfold capillary microscopy is used for diagnostic purposes in rheumatology since many years. Abnormal nailfold capillaries are also listed in the 2013 ACR/EULAR classification criteria for systemic sclerosis (SSc) [1]. A fast-track algorithm to differentiate between "scleroderma" and "non-scleroderma" patterns of capillary microscopy was published in 2019, and is being applied in clinical routine as a useful decision algorithm [2]. Artificial intelligence is being increasingly implemented in many spheres of everyday life, including medicine. Keras is a publicly available deep learning framework, which is widely used for research purposes, in particular for constructing artificial neural networks [3]. <h3>Objectives</h3> To estimate the ability of a deep learning model, to differentiate between scleroderma and non-scleroderma capillary microscopy patterns. <h3>Methods</h3> Capillary microscopy was performed using Optilia Digital Capillaroscope. We analyzed capillary microscopy images, which were equally subdivided into two groups, including scleroderma and non-scleroderma patterns. Subsequently the pictures were assigned to training, validation and test sets. Each set contained the same number of images with scleroderma- and non-scleroderma patterns. Then we trained a deep learning model using training and validation datasets to differentiate between the aforementioned capillaroscopy patterns. A test set was applied to estimate the model performance in an independent data set. The technical side of the study was performed using a 2D convolutional neural network, which was constructed based on Keras deep learning libraries (example Picture 1). <h3>Results</h3> A total of 1076 capillaroscopy images from 70 patients were analyzed the study: 780, 196 and 100 pictures in the training, validation and test sets accordingly. Each image dataset contained an equal amount of pictures from scleroderma- and non-scleroderma patterns. The trained model showed an accuracy of 87,8% with sensitivity of 94.9% (95% CI 0,905 to 0,99), specificity of 80,6% (95% CI 0,73 to 0,88) and an area under the curve (AUC) of 0.9326 in the validation set. In the test set, there was a slightly better model performance with an accuracy of 92%, a sensitivity of 96% (95% CI 0,91 to 1), a specificity of 88% (95% CI 0,79 to 0.97) and an AUC of 0,9742. <h3>Conclusion</h3> Despite the relatively small image dataset, the developed deep learning model could successfully discriminate scleroderma- vs. non-scleroderma capillaroscopy patterns, which may be applied in an automated assessment of capillaroscopy pictures in the future. <h3>References</h3> [1]Van Den Hoogen, Frank, et al. "2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League against Rheumatism collaborative initiative." Arthritis &amp; Rheumatism 65.11 (2013): 2737-2747. [2]Smith, Vanessa, et al. "Fast track algorithm: how to differentiate a "scleroderma pattern" from a "non-scleroderma pattern"." Autoimmunity reviews 18.11 (2019): 102394. [3]Gulli, Antonio, and Sujit Pal. Deep learning with Keras. Packt Publishing Ltd, 2017. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.